AI and ethics最新文献

{"title":"The psychology of machine intelligence: evaluating the necessity of artificial intelligence in everyday smart devices and automated systems","authors":"Swarnajit Bhattacharya","doi":"10.1007/s43681-026-01421-8","DOIUrl":"10.1007/s43681-026-01421-8","url":null,"abstract":"<div><p>The exponential rise of Artificial Intelligence (AI) has seamlessly woven intelligent systems into nearly every sphere of human life from smart homes and healthcare to transportation and entertainment. Yet, beneath this technological revolution lies a deeper psychological question: are humans truly benefiting from AI, or gradually examining the implications of increasing reliance on intelligent systems. Preliminary data suggests that over 78% of individuals in developed societies engage with AI-driven devices at least 12 times a day, while 62% admit to relying on automated systems for decisions once made intuitively. This study employs quantitative performance metrics and psychological dependency assessments across a diverse sample of 80 + participants to analyze the cognitive, emotional, and social implications of daily AI exposure. Comparative experiments between AI-assisted and manual task environments will measure productivity, satisfaction, and creative independence to determine whether constant AI support enhances or inhibits human potential. While the findings are expected to reveal fascinating insights into how humans psychologically adapt to machine intelligence, one crucial question remains unresolved does AI genuinely enrich human life, or subtly condition it for convenience at the cost of individuality. The answer, as the forthcoming analysis will uncover, may challenge the very foundation of our belief in progress.</p></div>","PeriodicalId":72137,"journal":{"name":"AI and ethics","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43681-026-01421-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating academic integrity in the Era of generative AI: a systematic literature review","authors":"Batool Hendal,&nbsp;Gobinda Chowdhury","doi":"10.1007/s43681-026-01431-6","DOIUrl":"10.1007/s43681-026-01431-6","url":null,"abstract":"<div><p>This study presents a comprehensive overview of existing research on the use of Generative Artificial Intelligence (GenAI) within academic contexts. It aims to provide a nuanced understanding of the evolving role of GenAI in higher education institutions by identifying the dominant themes and emphasising the recommendations proposed by researchers. To achieve this purpose, a Systematic Literature Review (SLR) was conducted, followed by an inductive thematic analysis to synthesise and interpret the findings. A convergent and divergent synthesis was subsequently undertaken to identify areas of agreement and debate across the reviewed literature. Based on 274 codes, six main themes were identified, including the potential benefits of GenAI, potential disadvantages of GenAI, GenAI and language, AI text detectors, policies and governance, and recommendations for responsible GenAI adoption. Grounded in the main recommendations identified across the literature, the study proposes an Academic GenAI Adaptation Framework, organised into two core dimensions: GenAI Governance and Infrastructure, and GenAI Education and Literacy. The framework provides higher education institutions with a practical roadmap for the strategic, ethical, and responsible integration of GenAI while safeguarding academic integrity.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":72137,"journal":{"name":"AI and ethics","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43681-026-01431-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Humanness not just a feature: AI, meaningful work, and the structural imperatives of flourishing","authors":"David Hampton-Musseau,&nbsp;Anne-Laure Gatignon-Turnau,&nbsp;Yann Landon","doi":"10.1007/s43681-026-01427-2","DOIUrl":"10.1007/s43681-026-01427-2","url":null,"abstract":"<div><p>The prompt absorption of artificial intelligence into organizational life raises a foundational question: not what AI does to jobs, but what it does to the conditions under which workers experience themselves as human. This paper advances the concept of humanness at work, defined as the capacity to experience oneself as an autonomous, competent, and socially recognized agent engaged in purposeful, relational contribution. Treated as the anthropological foundation from which AI governance and work design must be evaluated, humanness is not a secondary outcome of technological adoption but its proper governing criterion. The paper makes three theoretical contributions. First, it reconceptualizes meaningful work as the necessary structural condition through which humanness becomes organizationally possible, rather than a desirable psychological outcome or managerial instrument. Second, it introduces the humanness deficit to diagnose how AI-mediated arrangements systematically erode these conditions through three interconnected vectors: algorithmic control, achievement displacement, and passive substitution. Third, it develops the Digital Meaningful Job (DMJ), a multilevel sociotechnical framework extending the sociotechnical systems tradition, that positions humanness as the governing criterion for AI integration and operationalizes this commitment through four theoretical propositions. The paper concludes with a four-part research agenda, arguing that preserving humanness under AI conditions requires sustainable institutional arrangements that treat human flourishing as a non-negotiable design criterion rather than a residual consideration.</p></div>","PeriodicalId":72137,"journal":{"name":"AI and ethics","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: The delegation illusion: why deploying autonomous AI agents does not diminish principal responsibility","authors":"Kennedy Chun Yin Kong","doi":"10.1007/s43681-026-01424-5","DOIUrl":"10.1007/s43681-026-01424-5","url":null,"abstract":"","PeriodicalId":72137,"journal":{"name":"AI and ethics","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43681-026-01424-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Individual and group fairness assessments via counterfactual explanations","authors":"Federico Sabbatini,&nbsp;Roberta Calegari","doi":"10.1007/s43681-026-01411-w","DOIUrl":"10.1007/s43681-026-01411-w","url":null,"abstract":"<div><p>This study explores the potential of counterfactual explanations to assess artificial intelligence (AI) fairness, especially in critical decision-making systems. Predictive models may amplify biases inherent in data sets or algorithms, and given the absence of a universally accepted fairness metric, a case-specific approach becomes mandatory. Existing statistical fairness metrics may not capture all aspects that are relevant to a context-aware assessment of non-discrimination. The goal of this work is to define a measure of fairness for AI systems based on explainable artificial intelligence concepts. Specifically, it leverages the analysis of counterfactual explanations of individuals/groups and their comparison with similar individuals/groups. Compared to existing state-of-the-art works, the contributions are (i) extending the definition of individual fairness, not limiting unfairness to decisions based on sensitive attributes but also ensuring similar treatment amongst similar individuals; (ii) revisiting (and generalising) existing notions and introducing new, more refined notions of group fairness based on counterfactuals; (iii) defining quantitative fairness metrics that reflect the evidence gathered through the analysis/comparison of counterfactual explanations.</p></div>","PeriodicalId":72137,"journal":{"name":"AI and ethics","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43681-026-01411-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Triarchic corrigibility: a role-separated governance design pattern for AI-assisted decision-making","authors":"Markus Wagner","doi":"10.1007/s43681-026-01362-2","DOIUrl":"10.1007/s43681-026-01362-2","url":null,"abstract":"<div><p>Artificial intelligence (AI) alignment is often framed as the problem of making a model reliably pursue human intentions or values. That work is indispensable, but deployed systems also raise a governance problem: who controls information, private context, correction, escalation, and action? This conceptual article proposes <i>triarchic corrigibility</i> as a role-separated governance design pattern for AI-assisted decision-making. The pattern combines (1) an embodied and answerable human, or an accountable human decision body; (2) a strictly isolated local AI layer for private contextual continuity; and (3) an externally connected AI layer for current public-world evidence. Final authorization of consequential action remains human, while epistemic and procedural competences are deliberately distributed so that no component can unilaterally control framing, memory, procedure, and execution. The article specifies a normative basis, trust-domain separation, decision rights, human-mediated data flows, reciprocal challenge, escalation, affected-party recourse, proportionate audit, and exit procedures. It distinguishes personal and organizational instantiations, compares the pattern with single-assistant, local-only, edge–cloud, multi-agent, human-in-the-loop, meaningful-human-control, and polycentric approaches, and provides criteria and rejection conditions for evaluating its value. It also advances an evolutionary-symbiotic hypothesis: repeated, bounded dependence on complementary roles may make correction and cooperative continuity preferable to unilateral control while preserving interruption, replacement, and exit. A worked case illustrates the protocol. The result is a testable governance hypothesis whose capacity to reduce correlated failure, preserve privacy and human judgment, and sustain correction under residual uncertainty requires empirical evaluation.</p></div>","PeriodicalId":72137,"journal":{"name":"AI and ethics","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43681-026-01362-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trust, clarity, and contestability: an Islamic information ethics framework for public-sector AI","authors":"Madani Ahmiddouch,&nbsp;Abdelghni el amoumri","doi":"10.1007/s43681-026-01440-5","DOIUrl":"10.1007/s43681-026-01440-5","url":null,"abstract":"<div><p>Public-sector artificial intelligence increasingly mediates access to public services, social benefits, administrative decisions, and citizen-state interactions. While this transformation promises efficiency and consistency, it also raises ethical concerns about opacity, accountability gaps, data misuse, digital exclusion, and the weakening of human recourse. This article develops an Islamic information ethics framework for public-sector AI through a structured normative analysis of primary Islamic sources, selected principles of usul al-fiqh, contemporary Islamic ethics scholarship, and major global AI governance frameworks. Drawing on amanah, justice, bayan, removal of hardship, accountability, and the cross-cutting criterion of maslahah, it argues that administrative AI must be assessed not only by predictive performance or service speed, but also by its capacity to explain decisions, protect data, prevent bias, preserve access for vulnerable citizens, and provide meaningful human review. The distinctive contribution is to translate these ethical commitments into auditable institutional requirements and measurable indicators for data stewardship, explainability, fairness, accessibility, and contestability. An illustrative case study of automated social benefit eligibility shows how the framework can be applied in practice. The article concludes that ethically legitimate algorithmic administration is not one that merely automates public authority, but one that keeps public decisions explainable, contestable, corrigible, and institutionally answerable to the people affected by them.</p></div>","PeriodicalId":72137,"journal":{"name":"AI and ethics","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Governance and accountability in compassionate AI: a normative framework for responsible design","authors":"Bobga Tachu,&nbsp;Anne Ighade,&nbsp;Ganesh Yogeeswaran","doi":"10.1007/s43681-026-01412-9","DOIUrl":"10.1007/s43681-026-01412-9","url":null,"abstract":"<div><p>Artificial intelligence systems are increasingly expected to engage with people in ways that reflect emotional sensitivity and support human well-being. This shift has created growing interest in compassionate AI, a class of systems designed to detect distress and respond with care-oriented intent. Yet efforts to embed compassion into AI design and deployment are uneven and often ungoverned. Current responsible AI frameworks address fairness, privacy, transparency, and safety. Still, they do not clarify how compassion should operate as a system-level requirement or how accountability should be assigned when compassion fails [4]. This absence presents a theoretical and practical governance gap, particularly in settings such as healthcare, education, crisis response, and customer support, where users may attribute moral qualities to AI interactions [51]. This paper addresses this gap by developing a multi-level governance model for compassionate AI and a role-based accountability framework that assigns responsibility across designers, developers, deploying organizations, and regulatory actors. The proposed model integrates insights from affective computing, wellbeing-centered design, and organizational compassion research to articulate how compassion can be treated as a formal governance construct rather than an informal aspiration (Aizenberg et al., [5]). It explains how compassion should be embedded at design time, operationalized within AI systems, and monitored through organizational oversight and policy interventions (Aizenberg et al., 2020). The accountability framework specifies responsibility for different forms of compassion failure, including insufficient recognition of distress, biased responses, and misleading emotional cues. The paper advances compassionate AI scholarship by framing compassion as a structured governance requirement to guide system development, organizational practice, and regulatory evaluation. It also proposes directions for future research on the design of responsible and compassionate AI. Together, the governance model and accountability framework offer a conceptual foundation intended to support human dignity, mitigate harm, and encourage warranted trust in AI technologies.</p></div>","PeriodicalId":72137,"journal":{"name":"AI and ethics","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performative authoritarianism in AI ethics charter production: a configurational analysis of 22 Arab states","authors":"Anis Ben Brik","doi":"10.1007/s43681-026-01409-4","DOIUrl":"10.1007/s43681-026-01409-4","url":null,"abstract":"&lt;div&gt;&lt;p&gt;AI ethics charters proliferate across the Arab world even as the same states deploy AI surveillance against domestic populations. The standard ethics-washing critique cannot diagnose this configuration because it was developed in democratic and corporate contexts and treats charter-implementation gaps as a single phenomenon. This paper introduces a Performative Authoritarianism Framework that draws the misfire and abuse distinction in Austin [1], pulls in the comparative-politics literature on authoritarian-upgrading, and operationalises the digital authoritarianism scholarship through a discursive scoping matrix. Five hypotheses are tested on the full N=22 cross-section of Arab League states using objective regulatory indicators, the AI Global Surveillance Index from Feldstein, fuzzy-set qualitative comparative analysis, and a pre-specified test against the developmental-lag alternative. All five are supported, subject to the qualifications stated below. Of seven procedurally complete states (national strategy plus dedicated body), zero have enacted binding AI law. International ethics alignment co-occurs with documented AI surveillance deployment across the corpus (Spearman rho = 0.73, &lt;i&gt;p&lt;/i&gt; &lt; 0.001); the association is capacity-laden and does not reach significance within the institutionally capable subset, so the co-occurrence is reported as descriptive rather than as evidence of a sincere-commitment failure. Charter content covers consumer-protection domains at mean 0.90 of full coverage and regime-critical surveillance domains at 0.05 (Wilcoxon paired &lt;i&gt;p&lt;/i&gt; = 0.008); this scoping gap is shared with democratic instruments coded on the same rubric, so the feature distinctive to the authoritarian corpus is the joint configuration of consumer-scoped principles, documented deployment in the excluded domains, and the absence of a binding backstop. State capacity is the strongest among several broadly necessary conditions for AI governance quality (fsQCA consistency = 0.91, ahead of regulatory maturity at 0.89, with five further conditions between 0.80 and 0.86, a margin that small recalibrations reverse); the ordering is read against collinearity among capacity-related conditions and is not presented as identifying a single dispositive cause. The developmental-lag alternative is not supported within the present cross-section: years since strategy adoption shows no positive association with substantive completion (rho = − 0.045), and the United Arab Emirates has had nine years post-strategy without enacting binding AI law. Pre-specified ex ante criteria are stated for the cases of Egypt and Bahrain that may transition out of the abuse configuration in the next 36 months. The abuse reading is advanced as the best-supported account of this configuration against the developmental-lag and sincere-prioritisation alternatives, not as a demonstrated property of regime intentions, and the framework identifies the institutional conditions ","PeriodicalId":72137,"journal":{"name":"AI and ethics","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43681-026-01409-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From false attribution to synthetic personation: personational integrity and a residual tort for deepfake harms","authors":"Han Li,&nbsp;Xufeng Zhang,&nbsp;Yuke Meng,&nbsp;Shenghui Bao","doi":"10.1007/s43681-026-01405-8","DOIUrl":"10.1007/s43681-026-01405-8","url":null,"abstract":"<div><p>Deepfakes do not stand categorically outside defamation law. When synthetic media communicate a false factual attribution about an identifiable person and are understood in that way, ordinary defamation doctrine may provide a remedy. The harder cases are residual ones in which serious injury or interference persists even though the synthetic nature of the representation is known, no reasonable audience treats the represented event as authentic, or the central wrong is identity substitution rather than reputational belief. This paper reframes the legal problem around that distinction and identifies personational integrity as the distinct interest at stake: the interest of a living person in not having an identifiable embodiment of that person materially deployed to occupy the person’s role as speaker, actor, participant, endorser, or decision-maker in a seriously wrongful fabricated context. Personational integrity is narrower than a general right to control one’s image and is analytically distinct from reputation, privacy, publicity value, and emotional tranquility, although violations may also injure those interests. Building on this account, the paper proposes a technology-neutral tort of Synthetic Personation Harm (SPH). Liability requires materially fabricated personation, identification, lack of authorization, contextual wrongfulness, serious interference with personational integrity or substantial consequential harm, causation, and an appropriate fault showing; non-consent alone is insufficient. Expressive safeguards for news, education, historical representation, parody, satire, and transformative art are built into the framework, together with an anti-circumvention rule preventing SPH from being used merely to repackage a defamation claim. A revised decision algorithm does not depend on a deepfake-detector threshold, and an ordinal harm matrix separates the core personational interference from consequential losses. Applications to the Zelenskyy surrender deepfake, the 2024 Hong Kong video-conference fraud, a labeled intimate fabrication, and educational and parodic boundary cases illustrate overlap, residuality, and protected use.</p></div>","PeriodicalId":72137,"journal":{"name":"AI and ethics","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}